Scalable AI-assisted workflow management for detector design optimization using distributed computing
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97 MATHEMATICS AND COMPUTING↗
Engineering topics
Nagai, K.
Publications and source records attributed to Nagai, K..
Measurement of 𝐽∕𝜓and 𝜓(2𝑆)production in 𝑝 +𝑝and 𝑝 +𝑑interactions at 120 GeV
We report the p + p and p + d differential cross sections measured in the SeaQuest experiment for J/psi and psi(2S) production at 120GeV beam energy covering the forward x-Feynman (x(F)) range of 0.5 < x(F) < 0.9. The measured cross sections are in good agreement with theoretical calculations based on the nonrelativistic QCD (NRQCD) using the long-distance matrix elements deduced from a recent global analysis of proton- and pion-induced charmonium production data. The sigma(psi(2S))/sigma(J/psi) cross section ratios are found to increase as x(F) increases, indicating that the q (q) over bar. annihilation process has larger contributions in the psi(2S) production than the J/psi production. The sigma(pd)/2 sigma(pp) cross section ratios are observed to be significantly different for the DrellYan process and J/psi production, reflecting their different production mechanisms. We find that the sigma(pd)/2 sigma(pp) ratios for J/psi production at the forward x(F) region are sensitive to the (d) over bar/(u) over bar flavor asymmetry of the proton sea, analogous to the Drell-Yan process. The transverse momentum (p(T)) distributions for J/psi and psi(2S) production are also presented and compared with data collected at higher center-of-mass energies.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
AI-assisted detector design for the EIC (AID(2)E)
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.
97 MATHEMATICS AND COMPUTING↗
Estimation of combinatoric background in seaquest using an event-mixing method
All experiments observing dilepton pairs (e.g. e + e -, μ + μ - ) must confront the existence of a combinatoricbackground caused by the combining of tracks not arising from the same physics vertex. Some method must be devised to calculate and remove this background. In this document we describe a particular event-mixing method relying on many of the unique aspects of the SeaQuest spectrometer and data. The method described here calculates the combinatoric background with correct normalization; i.e., there is no need to assign a floating normalization factor that is then determined in a subsequent fitting procedure. Numerous tests are applied to demonstrate the reliability of the method.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Measurement of flavor asymmetry of the light-quark sea in the proton with Drell-Yan dimuon production in p + p and p + d collisions at 120 GeV
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Publisher Correction: The asymmetry of antimatter in the proton
The fundamental building blocks of the proton—quarks and gluons—have been known for decades. However, we still have an incomplete theoretical and experimental understanding of how these particles and their dynamics give rise to the quantum bound state of the proton and its physical properties, such as its spin1. The two up quarks and the single down quark that comprise the proton in the simplest picture account only for a few per cent of the proton mass, the bulk of which is in the form of quark kinetic and potential energy and gluon energy from the strong force2. An essential feature of this force, as described by quantum chromodynamics, is its ability to create matter–antimatter quark pairs inside the proton that exist only for a very short time. Their fleeting existence makes the antimatter quarks within protons difficult to study, but their existence is discernible in reactions in which a matter–antimatter quark pair annihilates. In this picture of quark–antiquark creation by the strong force, the probability distributions as a function of momentum for the presence of up and down antimatter quarks should be nearly identical, given that their masses are very similar and small compared to the mass of the proton3. Here we provide evidence from muon pair production measurements that these distributions are considerably different, with more abundant down antimatter quarks than up antimatter quarks over a wide range of momenta. These results are expected to revive interest in several proposed mechanisms for the origin of this antimatter asymmetry in the proton that had been disfavoured by previous results4, and point to future measurements that can distinguish between these mechanisms.
Dove, J.↗
The asymmetry of antimatter in the proton
The fundamental building blocks of the proton—quarks and gluons—have been known for decades. However, we still have an incomplete theoretical and experimental understanding of how these particles and their dynamics give rise to the quantum bound state of the proton and its physical properties, such as its spin1. The two up quarks and the single down quark that comprise the proton in the simplest picture account only for a few per cent of the proton mass, the bulk of which is in the form of quark kinetic and potential energy and gluon energy from the strong force2. An essential feature of this force, as described by quantum chromodynamics, is its ability to create matter–antimatter quark pairs inside the proton that exist only for a very short time. Their fleeting existence makes the antimatter quarks within protons difficult to study, but their existence is discernible in reactions in which a matter–antimatter quark pair annihilates. In this picture of quark–antiquark creation by the strong force, the probability distributions as a function of momentum for the presence of up and down antimatter quarks should be nearly identical, given that their masses are very similar and small compared to the mass of the proton. Here we provide evidence from muon pair production measurements that these distributions are considerably different, with more abundant down antimatter quarks than up antimatter quarks over a wide range of momenta. These results are expected to revive interest in several proposed mechanisms for the origin of this antimatter asymmetry in the proton that had been disfavoured by previous results, and point to future measurements that can distinguish between these mechanisms.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗